TY - GEN
T1 - Cellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma
AU - Chen, Pingjun
AU - Saad, Maliazurina B.
AU - Rojas, Frank R.
AU - Salehjahromi, Morteza
AU - Aminu, Muhammad
AU - Bandyopadhyay, Rukhmini
AU - Hong, Lingzhi
AU - Ebare, Kingsley
AU - Behrens, Carmen
AU - Gibbons, Don L.
AU - Kalhor, Neda
AU - Heymach, John V.
AU - Wistuba, Ignacio I.
AU - Solis Soto, Luisa M.
AU - Zhang, Jianjun
AU - Wu, Jia
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Pathology is the gold standard for cancer diagnosis. Numerous studies aim to automate the diagnosis based on digital slides, yet its prognostic utilities lack adequate investigation. Besides the inherent difficulties in predicting a patient’s prognosis, extracting informative features from gigapixel and heterogeneous whole slide images (WSI) remains an open challenge. We present a computational pipeline that can generate an embedded map to flexibly profile different cell populations’ local and global composition and architecture on WSIs. Our approach allows researchers to investigate tumor cells and tumor microenvironment based on these embedded maps of a reasonable size rather than dealing with gigantic WSIs. Here, we applied this pipeline to extract the texture patterns for tumor and immune cell types on the TCGA lung adenocarcinoma dataset. Based on extensive survival modeling, we have demonstrated that by pruning redundant and irrelevant features, the final prediction model has achieved an optimal C-index of 0.70 during testing. Our proof-of-concept study proves that the efficient local-global embedded maps bear valuable information with clinical correlations in lung cancer and potentially in other cancer types, warranting further investigations.
AB - Pathology is the gold standard for cancer diagnosis. Numerous studies aim to automate the diagnosis based on digital slides, yet its prognostic utilities lack adequate investigation. Besides the inherent difficulties in predicting a patient’s prognosis, extracting informative features from gigapixel and heterogeneous whole slide images (WSI) remains an open challenge. We present a computational pipeline that can generate an embedded map to flexibly profile different cell populations’ local and global composition and architecture on WSIs. Our approach allows researchers to investigate tumor cells and tumor microenvironment based on these embedded maps of a reasonable size rather than dealing with gigantic WSIs. Here, we applied this pipeline to extract the texture patterns for tumor and immune cell types on the TCGA lung adenocarcinoma dataset. Based on extensive survival modeling, we have demonstrated that by pruning redundant and irrelevant features, the final prediction model has achieved an optimal C-index of 0.70 during testing. Our proof-of-concept study proves that the efficient local-global embedded maps bear valuable information with clinical correlations in lung cancer and potentially in other cancer types, warranting further investigations.
KW - Cell architecture
KW - Lung adenocarcinoma
KW - Nuclei classification
KW - Survival analysis
KW - Whole slide image
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U2 - 10.1007/978-3-031-17266-3_1
DO - 10.1007/978-3-031-17266-3_1
M3 - Conference contribution
AN - SCOPUS:85140458809
SN - 9783031172656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 10
BT - Computational Mathematics Modeling in Cancer Analysis - 1st International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Qin, Wenjian
A2 - Zaki, Nazar
A2 - Zhang, Fa
A2 - Wu, Jia
A2 - Yang, Fan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 18 September 2022
ER -